One class of optimization algorithms that has been successfully applied to robotics is Bayesian optimization (BO) [1]}, [2]}, particularly due to its ability to optimize black-box objectives which are expensive to evaluate. [3]} introduce a general framework to incorporate inequality constraints in Bayesian optimization. Similarly, [4]}, [5]} propose safe Bayesian optimization in the context of parameter tuning in robotics. Nevertheless, BO does not scale well with the dimensionality of the parameters. Thus, on physical robots, BO is practically limited to optimizing around 10 parameters.
